Asthma-related productivity losses in Alberta, Canada

Nguyen X Thanh, Arto Ohinmaa, Charles YanInstitute of Health Economics, Edmonton, Alberta, CanadaObjectives: To estimate the number and cost of asthma-related productivity loss days due to absenteeism and presenteeism (at work but not fully functioning) in Alberta in 2005.Methods: Using data from the 2005 Canadian Community Health Survey, this study focused on people of working age (18–64 years), who reported having an asthma diagnosis. Total asthma-related disability days, including in-bed days and activity-restricted days, were estimated by multiplying the difference in the means of total disability days between asthmatics and nonasthmatics adjusted for sociodemographic characteristics and other health conditions by a multiple linear regression, with the number of asthmatics in the population. Number of productivity loss days was a sum between the number of in-bed days (absenteeism) and the number of activity-restricted days multiplied by a reduction in functional level (presenteeism), adjusted for five working days per week. Other data from Alberta or Canadian published literature, such as a reduction in functional level of 20%–30%, a labor participation rate of 73%, and an average wage of $158 per day in 2005, were also used for analyses.Results: The prevalence of asthma was estimated at 8.5$70 (41–99) to $84 ($49–$119) million. Of these, the presenteeism accounted for 42% to 52%.Conclusions: The results suggest that an improvement in the controlling of asthma could have a significant economic impact in Alberta and that presenteeism plays an important role in asthma-related productivity losses and therefore employers should not only pay attention to absenteeism, but also to presenteeism to minimize productivity loss.Keywords: productivity loss, absenteeism, presenteeism, asthma, Albert

Morphological characteristics and genetic diversity of <em>Terapon jarbua</em> (Forrskäl, 1775) in Central, Vietnam

Many environmental factors affect the morphology of migratory fish species, such as salinity, water flow rate, and temperature. However, studies on changes in fish morphology under environmental variations from salt water to brackish water are still limited in many fish species, especially in *Terapon jarbua*. This study aims to investigate the differences in the morphological parameters of *T. jarbua* between the coastal sea (seawater) and lagoon (brackish water); and between male and female fish based on a landmark morphological approach. Additionally, the genetic diversity of *T. jarbua* populations in Central Vietnam was elucidated using the mitochondrial DNA cytochrome oxidase subunit I (mtDNA COI) sequence as a molecular marker. The analytical results indicated no sexual dimorphism in the *T. jarbua* population, yet conformational differences exist between the two studied aquatic species. The analysis of 42 mtDNA COI sequences collected from Central Vietnam identified 13 haplotypes with medium genetic diversity and low genetic differentiation between the Tam Giang lagoon and Thua Thien Hue coastal (Fst = 0.028) and not significant (p = 0.126). Most haplotypes obtained are present in reference populations, indicating a high genetic exchange between populations. We proposed that the *T. jarbua* population in Central Vietnam has a stable connection with neighboring populations (China, Taiwan, Philippines, Indonesia, Malaysia, Bangladesh, India, and Pakistan)

Capacitance probe for fluid flow and volume measurements

Method and apparatus for making measurements on fluids are disclosed, including the use of a capacitive probe for measuring the flow volume of a material within a flow stream. The capacitance probe has at least two elongate electrodes and, in a specific embodiment of the invention, has three parallel elongate electrodes with the center electrode being an extension of the center conductor of a co-axial cable. A conductance probe is also provided to provide more accurate flow volume data in response to conductivity of the material within the flow stream. A preferred embodiment of the present invention provides for a gas flow stream through a microgravity environment that allows for monitoring a flow volume of a fluid sample, such as a urine sample, that is entrained within the gas flow stream

Method and Apparatus for Measuring Fluid Flow

Method and apparatus for making measurements on fluids related to their complex permeability are disclosed. A microwave probe is provided for exposure to the fluids. The probe can be non-intrusive or can also be positioned at the location where measurements are to be made. The impedance of the probe is determined. in part. by the complex dielectric constant of the fluids at the probe. A radio frequency signal is transmitted to the probe and the reflected signal is phase and amplitude detected at a rapid rate for the purpose of identifying the fluids. Multiple probes may be selectively positioned to monitor the behavior of the fluids including their flow rate. Fluids may be identified as between two or more different fluids as well as multiple phases of the same fluid based on differences between their complex permittivities

Capacitance Probe for Fluid Flow and Volume Measurements

Method and apparatus for making measurements on fluids are disclosed, including the use of a capacitive probe for measuring the flow volume of a material within a flow stream. The capacitance probe has at least two elongate electrodes and, in a specific embodiment of the invention, has three parallel elongate electrodes with the center electrode being an extension of the center conductor of a co-axial cable. A conductance probe is also provided to provide more accurate flow volume data in response to conductivity of the material within the flow stream. A preferred embodiment of the present invention provides for a gas flow stream through a micro-gravity environment that allows for monitoring a flow volume of a fluid sample, such as a urine sample, that is entrained within the gas flow stream

Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding

Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample similarities in the embedding space from an unlabeled dataset. Traditional UDML methods usually use the triplet loss or pairwise loss which requires the mining of positive and negative samples w.r.t. anchor data points. This is, however, challenging in an unsupervised setting as the label information is not available. In this paper, we propose a new UDML method that overcomes that challenge. In particular, we propose to use a deep clustering loss to learn centroids, i.e., pseudo labels, that represent semantic classes. During learning, these centroids are also used to reconstruct the input samples. It hence ensures the representativeness of centroids - each centroid represents visually similar samples. Therefore, the centroids give information about positive (visually similar) and negative (visually dissimilar) samples. Based on pseudo labels, we propose a novel unsupervised metric loss which enforces the positive concentration and negative separation of samples in the embedding space. Experimental results on benchmarking datasets show that the proposed approach outperforms other UDML methods.Comment: Accepted in BMVC 202

Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.Comment: 15 pages, 10 figures. A short version will be submitted to IEEE GLOBECOM 202